evo2 SAE eval (1/2): label producers — imported by #1636

.github/workflows/unit-tests-evo2-recipe-cpu.yaml

@@ -0,0 +1,53 @@
+name: "BioNeMo Evo2 SAE Recipe CI (CPU)"
+
+# CPU unit tests for the model-agnostic parts of the evo2 SAE recipe (DNA labelers, window
+# builders, eval metrics) — no model and no GPU, just CPU torch + numpy + biopython on
+# ubuntu-latest. The model-loading GPU tests (@pytest.mark.slow) run in the dedicated megatron
+# lane (unit-tests-interpretability-recipes.yaml); here they're excluded via -m "not slow".
+
+on:
+  push:
+    branches:
+      - "pull-request/[0-9]+"
+      - "dependabot/**"
+    paths:
+      - "interpretability/sparse_autoencoders/recipes/evo2/**"
+      - "interpretability/sparse_autoencoders/sae/**"
+      - ".github/workflows/unit-tests-evo2-recipe-cpu.yaml"
+  merge_group:
+    types: [checks_requested]
+  schedule:
+    - cron: "0 9 * * *" # Runs at 9 AM UTC daily (2 AM MST)
+
+defaults:
+  run:
+    shell: bash -x -e -u -o pipefail {0}
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  recipe-cpu-tests:
+    runs-on: ubuntu-latest
+    name: "evo2-recipe-tests (cpu)"
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          sparse-checkout: interpretability/sparse_autoencoders
+          sparse-checkout-cone-mode: false
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install (CPU torch + sae + recipe)
+        working-directory: interpretability/sparse_autoencoders
+        run: |
+          pip install --extra-index-url https://download.pytorch.org/whl/cpu -e sae -e recipes/evo2
+
+      - name: Run model-agnostic recipe tests
+        working-directory: interpretability/sparse_autoencoders/recipes/evo2
+        run: pytest -v tests/ -m "not slow"

interpretability/sparse_autoencoders/recipes/evo2/pyproject.toml

@@ -13,6 +13,8 @@ dependencies = [
     "torch>=2.0",
     "numpy>=1.20",
     "pyarrow>=23.0.0",
+    "biopython>=1.80",  # genetic code / translation in labelers.py
+    "pyrodigal>=3.0",  # prokaryotic gene calling in labelers.predict_cds / predict_codons
 ]
 
 # No package code lives here yet — the recipe is just an entry-point for
@@ -23,3 +25,10 @@ packages = []
 
 [tool.uv.sources]
 sae = { workspace = true }
+
+# Register markers locally so the recipe is self-contained (the repo-root pytest.ini isn't in the
+# CI sparse-checkout); lets the CPU lane select `-m "not slow"` without an unknown-marker warning.
+[tool.pytest.ini_options]
+markers = [
+    "slow: GPU/integration tests that load a model (skip without CUDA + checkpoints)",
+]

interpretability/sparse_autoencoders/recipes/evo2/scripts/annot_tracks.py

@@ -0,0 +1,146 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+r"""Generic interval-track loader for the "user-supplied annotated dataset" eval.
+
+The user hands in an annotated dataset: a FASTA of sequences + one or more annotation
+tracks (BED or GFF) naming intervals — RefSeq genes/exons, Rfam ncRNA, JASPAR TFBS,
+ENCODE cCREs, etc. Each interval is one annotation **instance**. This module tiles the
+sequences into windows and produces, per concept, a per-token boolean mask + per-token
+**global** instance IDs (stable across the windows an interval spans) — exactly the
+inputs `sae.eval.probing.domain_f1` (recall-per-instance) and `auroc_all` (per-feature)
+consume. No model here; the SAE-encode step lives in the probe CLI (`probe.py domain-eval`).
+
+This is the generic sibling of `euk_windows.py` (which decomposes RefSeq gene models into
+exon/intron/cds). Both feed the same shared scorers.
+"""
+
+from __future__ import annotations
+
+import gzip
+from collections import defaultdict
+
+
+def _open(path):
+    """Open a path for text reading, transparently handling ``.gz``."""
+    return (gzip.open if str(path).endswith(".gz") else open)(path, "rt")
+
+
+def read_fasta_dict(path: str) -> dict[str, str]:
+    """Read a (multi-record) FASTA into ``{seq_id: sequence}`` (``.gz`` transparent).
+
+    ``seq_id`` is the first whitespace token of the header — matches the chrom/seqid of BED/GFF.
+    """
+    seqs: dict[str, str] = {}
+    name, parts = None, []
+    with _open(path) as fh:
+        for line in fh:
+            line = line.rstrip()
+            if line.startswith(">"):
+                if name is not None:
+                    seqs[name] = "".join(parts)
+                tok = line[1:].split()
+                name, parts = (tok[0] if tok else f"seq_{len(seqs)}"), []
+            elif line:
+                parts.append(line)
+    if name is not None:
+        seqs[name] = "".join(parts)
+    return seqs
+
+
+def _intervals(path, fmt, feature_type=None):
+    """Yield (seqid, start0, end0) from BED (0-based) or GFF/GTF (1-based -> 0-based half-open).
+
+    GFF rows are optionally filtered to a single column-3 ``feature_type`` (e.g. ``exon``).
+    """
+    chrom_i, start_i, end_i, off = (0, 1, 2, 0) if fmt == "bed" else (0, 3, 4, 1)
+    with _open(path) as fh:
+        for line in fh:
+            if not line.strip() or line[0] == "#" or line.startswith(("track", "browser")):
+                continue
+            f = line.split("\t")
+            if len(f) <= end_i or (feature_type and fmt != "bed" and f[2] != feature_type):
+                continue
+            yield f[chrom_i], int(f[start_i]) - off, int(f[end_i])
+
+
+def load_track(path, feature_type=None, fmt=None):
+    """Load one annotation track into ``{seqid: [(start0, end0), ...]}`` (0-based half-open, sorted).
+
+    ``fmt`` (``bed``/``gff``) is inferred from the extension; ``feature_type`` filters GFF column 3.
+    Every interval is one annotation instance.
+    """
+    fmt = fmt or ("gff" if str(path).replace(".gz", "").endswith((".gff", ".gff3", ".gtf")) else "bed")
+    by_seq = defaultdict(list)
+    for chrom, s, e in _intervals(path, fmt, feature_type):
+        if e > s:
+            by_seq[chrom].append((s, e))
+    return {k: sorted(v) for k, v in by_seq.items()}
+
+
+def label_windows(seqs, tracks, seq_len=1024, stride=None, max_tokens=None, min_n_frac=0.5):
+    """Tile sequences into windows, labeling each position per concept (mask + global instance id).
+
+    Args:
+        seqs: ``{seqid: dna_str}``.
+        tracks: ``{concept: {seqid: [(start0, end0), ...]}}`` (e.g. from `load_track`).
+        seq_len: window length in bp.
+        stride: step between windows (defaults to non-overlapping = seq_len).
+        max_tokens: stop once this many positions are emitted (None = all).
+        min_n_frac: skip windows whose ``N`` fraction exceeds this.
+
+    Returns:
+        (windows, stats). Each window is ``{"dna": str, "labels": {concept: bool[L]},
+        "instances": {concept: int32[L]}}``. Each interval gets one global id, stable across
+        the windows it spans, so `domain_f1`'s recall-per-instance counts a split interval once.
+    """
+    import numpy as np
+
+    stride = stride or seq_len
+    concepts = list(tracks.keys())
+    # assign a global instance id to every interval, per concept
+    concept_iv: dict[str, dict[str, list[tuple[int, int, int]]]] = {}
+    n_inst: dict[str, int] = {}
+    for concept in concepts:
+        gid = 0
+        cc: dict[str, list[tuple[int, int, int]]] = {}
+        for seqid, ivs in tracks[concept].items():
+            cc[seqid] = [(s, e, (gid := gid + 1) - 1) for (s, e) in ivs]
+        concept_iv[concept] = cc
+        n_inst[concept] = gid
+
+    windows, tot = [], 0
+    for seqid, dna in seqs.items():
+        dna = dna.upper()
+        N = len(dna)
+        for w0 in range(0, max(1, N - seq_len + 1), stride):
+            w1 = min(N, w0 + seq_len)
+            sub = dna[w0:w1]
+            L = w1 - w0
+            if L < 60 or sub.count("N") > min_n_frac * L:
+                continue
+            labels = {c: np.zeros(L, bool) for c in concepts}
+            inst = {c: np.full(L, -1, np.int32) for c in concepts}
+            for c in concepts:
+                for s, e, gid in concept_iv[c].get(seqid, []):
+                    if e <= w0 or s >= w1:
+                        continue
+                    labels[c][max(s, w0) - w0 : min(e, w1) - w0] = True
+                    inst[c][max(s, w0) - w0 : min(e, w1) - w0] = gid
+            windows.append({"dna": sub, "labels": labels, "instances": inst})
+            tot += L
+            if max_tokens and tot >= max_tokens:
+                return windows, {"tokens": tot, "n_inst": n_inst, "concepts": concepts}
+    return windows, {"tokens": tot, "n_inst": n_inst, "concepts": concepts}